With the above priors at hand, we are now in a position to calculate probabilities relevant to Bayesian inference. In this Section we focus on the prior predictive distribution \(P(I)\) ,
which gives the probability to occupy vacuum \(I\)  irrespective of the time of existence or ancestral vacuum. We will find that the resulting probabilities \(P(I|{\cal H}_{\rm uni})\) 
and \(P(I|{\cal H}_{\rm late})\)  coincide with two measures proposed in the literature, respectively the holographic prior probabilities [1]} and the quasi-stationary measure of GSVW [2]}.
Furthermore, each has a close analogue among centrality indices studied in network science.
